Landsat-8 Sea Ice Classification Using Deep Neural Networks
Knowing the location and type of sea ice is essential for safe navigation and route optimization in ice-covered areas. In this study, we developed a deep neural network (DNN) for pixel-based ice Stage of Development classification for the Baltic Sea using Landsat-8 optical satellite imagery to provi...
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ftmdpi:oai:mdpi.com:/2072-4292/14/9/1975/ 2023-08-20T04:09:43+02:00 Landsat-8 Sea Ice Classification Using Deep Neural Networks Alvaro Cáceres Egbert Schwarz Wiebke Aldenhoff agris 2022-04-20 application/pdf https://doi.org/10.3390/rs14091975 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing Image Processing https://dx.doi.org/10.3390/rs14091975 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 14; Issue 9; Pages: 1975 Landsat-8 deep neural networks sea ice classification Text 2022 ftmdpi https://doi.org/10.3390/rs14091975 2023-08-01T04:48:36Z Knowing the location and type of sea ice is essential for safe navigation and route optimization in ice-covered areas. In this study, we developed a deep neural network (DNN) for pixel-based ice Stage of Development classification for the Baltic Sea using Landsat-8 optical satellite imagery to provide up-to-date ice information for Near-Real-Time maritime applications. In order to train the network, we labeled the ice regions shown in the Landsat-8 imagery with classes from the German Federal Maritime and Hydrographic Agency (BSH) ice charts. These charts are routinely produced and distributed by the BSH Sea Ice Department. The compiled data set for the Baltic Sea region consists of 164 ice charts from 2014 to 2021 and contains ice types classified by the Stage of Development. Landsat-8 level 1 (L1b) images that could be overlaid with the available BSH ice charts based on the time of acquisition were downloaded from the United States Geological Survey (USGS) global archive and indexed in a data cube for better handling. The input variables of the DNN are the individual spectral bands: aerosol coastal, blue, green, red and near-infrared (NIR) out of the Operational Land Imager (OLI) sensor. The bands were selected based on the reflectance and emission properties of sea ice. The output values are 4 ice classes of Stage of Development and Ice Free. The results obtained show significant improvements compared to the available BSH ice charts when moving from polygons to pixels, preserving the original classes. The classification model has an accuracy of 87.5% based on the test data set excluded from the training and validation process. Using optical imagery can therefore add value to maritime safety and navigation in ice- infested waters by high resolution and real-time availability. Furthermore, the obtained results can be extended to other optical satellite imagery such as Sentinel-2. Our approach is promising for automated Near-Real-Time (NRT) services, which can be deployed and integrated at a later stage at the ... Text Sea ice ice covered areas MDPI Open Access Publishing Remote Sensing 14 9 1975 |
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Open Polar |
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MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
Landsat-8 deep neural networks sea ice classification |
spellingShingle |
Landsat-8 deep neural networks sea ice classification Alvaro Cáceres Egbert Schwarz Wiebke Aldenhoff Landsat-8 Sea Ice Classification Using Deep Neural Networks |
topic_facet |
Landsat-8 deep neural networks sea ice classification |
description |
Knowing the location and type of sea ice is essential for safe navigation and route optimization in ice-covered areas. In this study, we developed a deep neural network (DNN) for pixel-based ice Stage of Development classification for the Baltic Sea using Landsat-8 optical satellite imagery to provide up-to-date ice information for Near-Real-Time maritime applications. In order to train the network, we labeled the ice regions shown in the Landsat-8 imagery with classes from the German Federal Maritime and Hydrographic Agency (BSH) ice charts. These charts are routinely produced and distributed by the BSH Sea Ice Department. The compiled data set for the Baltic Sea region consists of 164 ice charts from 2014 to 2021 and contains ice types classified by the Stage of Development. Landsat-8 level 1 (L1b) images that could be overlaid with the available BSH ice charts based on the time of acquisition were downloaded from the United States Geological Survey (USGS) global archive and indexed in a data cube for better handling. The input variables of the DNN are the individual spectral bands: aerosol coastal, blue, green, red and near-infrared (NIR) out of the Operational Land Imager (OLI) sensor. The bands were selected based on the reflectance and emission properties of sea ice. The output values are 4 ice classes of Stage of Development and Ice Free. The results obtained show significant improvements compared to the available BSH ice charts when moving from polygons to pixels, preserving the original classes. The classification model has an accuracy of 87.5% based on the test data set excluded from the training and validation process. Using optical imagery can therefore add value to maritime safety and navigation in ice- infested waters by high resolution and real-time availability. Furthermore, the obtained results can be extended to other optical satellite imagery such as Sentinel-2. Our approach is promising for automated Near-Real-Time (NRT) services, which can be deployed and integrated at a later stage at the ... |
format |
Text |
author |
Alvaro Cáceres Egbert Schwarz Wiebke Aldenhoff |
author_facet |
Alvaro Cáceres Egbert Schwarz Wiebke Aldenhoff |
author_sort |
Alvaro Cáceres |
title |
Landsat-8 Sea Ice Classification Using Deep Neural Networks |
title_short |
Landsat-8 Sea Ice Classification Using Deep Neural Networks |
title_full |
Landsat-8 Sea Ice Classification Using Deep Neural Networks |
title_fullStr |
Landsat-8 Sea Ice Classification Using Deep Neural Networks |
title_full_unstemmed |
Landsat-8 Sea Ice Classification Using Deep Neural Networks |
title_sort |
landsat-8 sea ice classification using deep neural networks |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14091975 |
op_coverage |
agris |
genre |
Sea ice ice covered areas |
genre_facet |
Sea ice ice covered areas |
op_source |
Remote Sensing; Volume 14; Issue 9; Pages: 1975 |
op_relation |
Remote Sensing Image Processing https://dx.doi.org/10.3390/rs14091975 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs14091975 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
9 |
container_start_page |
1975 |
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1774723350697869312 |